What does it take to evolve an enhancer? A simulation-based study of factors influencing the emergence of combinatorial regulation.

Duque T, Sinha S - Genome Biol Evol (2015)

Bottom Line:
There is widespread interest today in understanding enhancers, which are regulatory elements typically harboring several transcription factor binding sites and mediating the combinatorial effect of transcription factors on gene expression.We found the time-to-evolve to range between 0.5 and 10 Myr, and to vary greatly with the target expression pattern, complexity of the real enhancer known to encode that pattern, and the strength of input from specific transcription factors.Our simulations also revealed that certain features of an enhancer might evolve not due to their biological function but as aids to the evolutionary process itself.

evv080-F2: Visual representation of real and in silico evolved CRMs. A two-dimensional projection of the six-dimensional “TF occupancy” space occupied by these CRMs. The axes represent the first and second principal components. The panels correspond to CRMs for patterns eve_1_ru (A), “run_stripe1” (B), “run_stripe5” (C), “eve_stripe5” (D), kni_83_ru (E), and “h_15_ru” (F). In each panel, simulated CRMs of respective pattern are shown in small black circles, and the real Drosophila melanogaster CRM for that pattern as a larger black circle; points in other colors represent simulated CRMs (smaller icons) and the real CRM (larger icon, same color) for other target patterns.

Mentions:
We first examined all in silico evolved CRMs for all 28 target patterns and noted that CRMs associated with similar expression patterns are closer to each other than distinctly expressed CRMs (supplementary fig. S2, Supplementary Material online), as expected. We then asked if in silico evolved CRMs for the same target pattern cluster in the vector space, and how tight these clusters are. Table 1 presents two relevant metrics to answer these questions. The first metric, dintra, represents the average distance between any pair of evolved CRM for a particular target pattern, and a second metric, dinter, denotes the average distance between CRMs for a specific expression pattern and CRMs representing other patterns. (We restricted the other patterns to be those that are least correlated with that pattern, because several of the target patterns are highly similar to each other.) As table 1 shows, the ratio dinter/dintra is almost always ≥2, indicating that distinct target patterns are associated with well-clustered simulated CRMs. A few examples are depicted in figure 2 (note black circles in each panel), which further confirms this observation.Table 1

evv080-F2: Visual representation of real and in silico evolved CRMs. A two-dimensional projection of the six-dimensional “TF occupancy” space occupied by these CRMs. The axes represent the first and second principal components. The panels correspond to CRMs for patterns eve_1_ru (A), “run_stripe1” (B), “run_stripe5” (C), “eve_stripe5” (D), kni_83_ru (E), and “h_15_ru” (F). In each panel, simulated CRMs of respective pattern are shown in small black circles, and the real Drosophila melanogaster CRM for that pattern as a larger black circle; points in other colors represent simulated CRMs (smaller icons) and the real CRM (larger icon, same color) for other target patterns.

Mentions:
We first examined all in silico evolved CRMs for all 28 target patterns and noted that CRMs associated with similar expression patterns are closer to each other than distinctly expressed CRMs (supplementary fig. S2, Supplementary Material online), as expected. We then asked if in silico evolved CRMs for the same target pattern cluster in the vector space, and how tight these clusters are. Table 1 presents two relevant metrics to answer these questions. The first metric, dintra, represents the average distance between any pair of evolved CRM for a particular target pattern, and a second metric, dinter, denotes the average distance between CRMs for a specific expression pattern and CRMs representing other patterns. (We restricted the other patterns to be those that are least correlated with that pattern, because several of the target patterns are highly similar to each other.) As table 1 shows, the ratio dinter/dintra is almost always ≥2, indicating that distinct target patterns are associated with well-clustered simulated CRMs. A few examples are depicted in figure 2 (note black circles in each panel), which further confirms this observation.Table 1

Bottom Line:
There is widespread interest today in understanding enhancers, which are regulatory elements typically harboring several transcription factor binding sites and mediating the combinatorial effect of transcription factors on gene expression.We found the time-to-evolve to range between 0.5 and 10 Myr, and to vary greatly with the target expression pattern, complexity of the real enhancer known to encode that pattern, and the strength of input from specific transcription factors.Our simulations also revealed that certain features of an enhancer might evolve not due to their biological function but as aids to the evolutionary process itself.